Method and system for detecting touch events based on magnitude ratios

ABSTRACT

A method for detecting a touch event on a touch panel comprises obtaining at least first and second signals from at least two sensors where the at least first and second signals are responsive to a touch event. A first amplitude magnitude associated with the first signal is calculated and a second amplitude magnitude associated with the second signal is calculated. A magnitude ratio is determined between the first and second amplitude magnitudes, and a touch location is identified based on the magnitude ratio.

BACKGROUND OF THE INVENTION

This invention relates generally to touch sensitive systems, and more particularly to identifying a location of a touch on a touch sensitive system that uses a bending-wave touch panel.

Touch panels are used to provide two-dimensional coordinate information. One example may be an opaque track pad while another example may be a transparent touchscreen placed in front of a display such as a liquid crystal display. Touch panels may be based on a variety of touch technologies including four-wire and five-wire resistive, capacitive, infrared and surface acoustic wave types, as well as bending-wave touch technologies.

Bending waves may also be referred to as “flexural waves” as well as “lowest order anti-symmetric Lamb waves” which is sometimes abbreviated as “A₀.” Bending waves in a plate, such as a touchscreen substrate, are characterized by motion that is largely perpendicular to the surface and essentially the same at all depths with respect to the surface. For example, touch detection has been proposed via finger absorption of ultrasonic frequency Lamb waves in the MHz frequency range. However, most recent work with bending wave touch systems uses frequencies well below the MHz range including the audio range.

In one approach in International Publication Number WO 00/38104 (Intelligent Vibrations, published 2000), bending wave touch systems may detect a touch based on a tap of an object, such as a key or finger, used to excite bending waves in a substrate. These bending waves induce electrical signals in piezoelectric elements or sensors (piezos) bonded to the substrate. These signals are captured by electronics and processed to determine (X,Y) coordinates of the touch position, such as by using time-of-flight methods to extract touch coordinate information from piezo signals.

An approach in International Publication Number WO 01/43063 (Soundtouch Limited, published 2001) may be described as “acoustic fingerprinting”. Templates are acquired that correspond to frequency transforms of piezo signals resulting from calibration touches on a substrate at various distances “D” from a piezo that is bonded to the substrate. In one example, each template has a horizontal axis representing frequency and a vertical axis representing normalized amplitude magnitude. During real time touch operation of the system, a similar normalized-amplitude-versus-frequency profile (as discussed further below) is generated for a touch event and compared with a library of templates. The distance from the touch to the piezo is determined by the value of “D” corresponding to the best matching template.

In another approach discussed in British Publication Number GB 2385125 (Hardie-Bick, published 2003), a normalized amplitude versus frequency profile is computed from a live touch and compared to a library of template profiles. The template with the best matching score is found. In this case, however, the normalized amplitude profiles are not just a function of the distance “D” between the touch and a piezo, but rather a function of (X,Y) touch coordinates in two dimensions. The normalized amplitudes, however, are sensitive to the details of the power spectrum of the touch force which varies with the style and implement used to make the touch. Therefore, the normalized amplitudes are sensitive to factors other than the (X, Y) coordinate location of the touch.

In another approach, International Publication Number WO03/067511 (Soundtouch Limited, published 2003), identifies touch locations using phase-difference profiles. The following mathematical notation is provided to more precisely define the terms normalized amplitude profiles and phase-difference profiles.

In one example, an operator touches a touch panel having four piezos from which two analog signals are generated. (In other examples, more or less than four piezos, sensors or microphones may be used.) Each of the signals, namely S₁(t) and S₂(t), are functions of time “t”. The signals S₁(t) and S₂(t) are digitized and frequency transformed, resulting in corresponding signals S₁(ω) and S₂(ω) in the frequency domain where ω=2πf is angular frequency. Any function or algorithm based on angular frequency ω is easily converted to a function or algorithm based on frequency f and vice versa; below the term “frequency” is interpreted broadly to include both frequency and angular frequency. For example, S₁(ω) and S₂(ω) may represent the frequency transforms of S₁(t) and S₂(t). By way of example only, S₁(ω) and S₂(ω) may be computed as a discrete frequency transform over a finite time interval such as provided by a Fourier transform or FFT (Fast Fourier Transform). S₁(t) and S₂(t) are real functions, while S₁(ω) and S₂(ω) are complex functions with both real parts Re{S₁(ω)} and Re{S₂(ω)} as well as imaginary parts Im{S₁(ω)} and Im{S₂(ω)}. This gives a total of four real functions of frequency. Such a complex number representation is required because at each frequency, the signal amplitude has both a magnitude and a phase. The complex functions S₁(ω) and S₂(ω) may be represented as products of a positive real magnitude and a complex phase factor of unit magnitude as shown below in Equation (Equ.) 1 and Equ. 2.

S ₁(ω)=A ₁(ω)·e ^(iφ)1^((ω)) =A ₁(ω)·cos(φ₁(ω))+i·A ₁(ω)·sin(φ₁(ω))  Equ. 1

S ₂(ω)=A ₂(ω)·e ^(iφ)2^((ω)) =A ₂(ω)·cos(φ₂(ω))+i·A ₂(ω)·sin(φ₂(ω))  Equ. 2

Here A₁(ω)=|S₁(ω)| and A₂(ω)=|S₂(ω)| are the positive real magnitudes of the signal amplitudes, φ₁(ω) and φ₂(ω) are the phases of the complex amplitudes S₁(ω) and S₂(ω), and “i” represents the square root of negative one. For Equ. 3 and 4 below, A₁ ^(MAX) is the maximum value of the amplitude magnitude A₁(ω) over the frequency range and A₂ ^(MAX) can be similarly defined. Equ. 3 and Equ. 4 define the normalized amplitude profiles a₁(ω) and a₂(ω) for the signals S₁(t) and S₂(t), wherein a₁(ω) and a₂(ω) are normalized to have maximum values of one.

a ₁(ω)=A ₁(ω)/A ₁ ^(MAX)  Equ. 3

a ₂(ω)=A ₂(ω)/A ₂ ^(MAX)  Equ. 4

Equ. 5 defines the phase difference profile Δφ(ω).

Δφ(ω)=φ₂(ω)−φ₁(ω)  Equ. 5

For clarity of presentation the above functions of frequency such as the amplitude magnitudes A₁(ω) and A₂(ω) and the phase difference Δφ(ω) are represented as continuous functions of frequency ω. In a practical engineering implementation, the mathematical continuum of frequencies ω is replaced by a finite set of discrete frequencies, for example, ω_(k) where the integer index k ranges from one to a perhaps large but finite integer K. Likewise, continuous functions of frequency are replaced by sets of values, such as A₁(ω_(k)) corresponding to the discrete frequencies ω_(k).

It has been observed that the phase difference profile Δφ(ω) performs much better as an acoustic fingerprint than the normalized amplitudes a₁(ω) and a₂(ω). For example, ΔΦ(ω) represents a template phase difference profile corresponding to a calibration touch at a known location (X₀,Y₀). When the live acoustic fingerprint of the phase difference profile Δφ(ω) is a close match to a template phase difference profile ΔΦ(ω) (Φ indicating calibration signal and φ indicating live signal), it is indicated that the coordinates (X,Y) of the live touch are close to the coordinates (X₀,Y₀) of the calibration touch that generated the template phase difference profile ΔΦ(ω).

As currently demonstrated by existing products, bending-wave touch systems based on acoustic fingerprinting provide good touch performance in combination with a number of other desirable product features such as high optical transparency, wear resistance, immunity to water contaminants and compact mechanical design. This is particularly true for larger sized systems, such as desktop sized touch/display systems based on, for example, 15-inch (approximately 380 mm) diagonal liquid crystal displays. However for some applications, there remains a need for improved acoustic fingerprint definitions and matching algorithms. In particular, for smaller sensor sizes, such as those used in handheld applications and point-of-sale applications, the phase difference profile Δφ(ω) by itself is not always sufficient to reliably extract touch coordinates when the signals S₁(t) and S₂(t) are subject to ambient noise. Furthermore, in some applications, brief moments of corrupted signal data can be a major problem due to generation of false touch coordinates.

There remains a need for improved acoustic fingerprints and matching algorithms, as well as improved identification of corrupted signal data, for use in bending-wave touch systems.

BRIEF DESCRIPTION OF THE INVENTION

In one embodiment, a method of detecting a touch event on a touch panel comprises obtaining at least first and second signals from at least two sensors where the at least first and second signals are responsive to a touch event. A first amplitude magnitude associated with the first signal is calculated and a second amplitude magnitude associated with the second signal is calculated. A magnitude ratio is determined between the first and second amplitude magnitudes, and a touch location is identified based on the magnitude ratio.

In another embodiment, a touch system comprises a touch panel and at least two sensors to obtain at least two signals associated with a touch event on the touch panel. A processor module performs frequency transforms based on the at least two signals to form frequency transform data sets and produces amplitude magnitudes based on the frequency transform data sets. The processor module compares a ratio of the amplitude magnitudes associated with at least two of the frequency transform data sets to a calibration file of template fingerprints associated with known locations on the touch panel to determine a coordinate location associated with the touch event. The template fingerprints are at least in part based on magnitude ratios.

In yet another embodiment, a computer readable medium for use in an acoustic fingerprint based touch system comprises instructions to process digitized signals from at least two sensors where the digitized signals are responsive to a touch event on a touch panel. The digitized signals comprise at least first and second signals. The computer readable medium has instructions to calculate a first amplitude magnitude associated with the first signal and a second amplitude magnitude associated with the second signal. Instructions are also provided to identify a touch location associated with the touch event based on a magnitude ratio that is based on the first and second amplitude magnitudes.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an acoustic fingerprint based touch system formed in accordance with an embodiment of the present invention.

FIG. 2 illustrates a method for determining a location of a live touch event on the touch panel of FIG. 1 based on a single acoustic fingerprint constructed using magnitude ratios in accordance with an embodiment of the present invention.

FIG. 3 illustrates a method for determining a location of a live touch event on the touch panel of FIG. 1 based on two acoustic fingerprints wherein one fingerprint is constructed using magnitude ratios and another is constructed using phase differences in accordance with an embodiment of the present invention.

FIG. 4 illustrates a method for determining a location of a live touch event on the touch panel of FIG. 1 based on a single gauge-invariant acoustic fingerprint incorporating both magnitude-ratio and phase-difference information in accordance with an embodiment of the present invention.

FIG. 5 illustrates a spherical coordinate system for use with acoustic fingerprint algorithms in accordance with an embodiment of the present invention.

FIG. 6 illustrates mapping of a magnitude ratio to a latitude angle in accordance with an embodiment of the present invention.

FIG. 7 illustrates an acoustic fingerprint based touch system formed in accordance with an embodiment of the present invention that has a touch panel that generates two or more signals to be used for touch detection.

FIG. 8 illustrates a method for determining a location of a live touch event on the touch panel of FIG. 7 based on one acoustic fingerprint that is constructed based on two or more digitized signals in accordance with an embodiment of the present invention

FIG. 9 illustrates a method for determining a location of a live touch event on the touch panel of FIG. 7 based on two or more acoustic fingerprints in accordance with an embodiment of the present invention

DETAILED DESCRIPTION OF THE INVENTION

The foregoing summary, as well as the following detailed description of certain embodiments of the present invention, will be better understood when read in conjunction with the appended drawings. To the extent that the figures illustrate diagrams of the functional blocks of various embodiments, the functional blocks are not necessarily indicative of the division between hardware circuitry. Thus, for example, one or more of the functional blocks (e.g., processors or memories) may be implemented in a single piece of hardware (e.g., a general purpose signal processor or random access memory, hard disk, or the like). Similarly, the programs may be stand alone programs, may be incorporated as subroutines in an operating system, may be functions in an installed software package, and the like. It should be understood that the various embodiments are not limited to the arrangements and instrumentality shown in the drawings.

FIG. 1 illustrates an acoustic fingerprint based touch system 100. The acoustic fingerprint based touch system 100 is an example of a bending-wave touch system. In one embodiment, the acoustic fingerprint based touch system 100 is a system in which the bending waves are touch generated and detected in the audio frequency range of 0-20 kHz and/or the low ultrasonic frequency range well below 1 MHz.

Touch panel 102 has a substrate 104, such as a sheet of glass, aluminum or other material, upon which sensors 106, 108, 110 and 112 and associated traces 114, 116, 118 and 120 are mounted. The sensors may be piezoelectric sensors, which may be abbreviated as “piezo sensors” or simply “piezos”. The sensors may also be any other type of sensor that detects local motion or strain of substrate 104 such as accelerometers and strain gauges of various types. The sensors 106, 108, 110 and 112 may also be referred to as microphones. The sensors 106, 108, 110 and 112 detect sound and transmit sensor signals along the traces 114, 116, 118 and 120 which interface with a touchscreen cable 122 to convey the sensor signals to a touchscreen controller 124. In the embodiment shown in FIG. 1, the voltage difference between traces 114 and 120 is one analog signal generated by the anti-series combination of sensors 106 and 112. Similarly, the anti-series combination of sensors 108 and 110 produces an analog signal corresponding to the voltage difference between traces 116 and 118. Alternatively, sensors 112 and 110 may optionally be removed such that each of sensors 106 and 108 corresponds to one analog signal. In other embodiments, more than two analog signals are produced with either altered trace interconnections and/or additional sensors (not shown). It should be understood that the sensors 106, 108, 110 and 112 are not limited to the displayed placement with respect to the substrate 104 and/or each other.

The touch panel 102, when used as a touchscreen, is formed of a non-opaque material such as glass and is mounted in front of a display 160 that may support a graphical user interface (GUI) displaying buttons and icons or other graphical representations. In other embodiments, the touch panel 102 may be formed of an opaque or non-opaque material and may be located physically separate from display 160, such as to function as a track pad. It should be understood that although the touch panel 102 is primarily referred to in the following discussion, other touch panel, touchscreen and track pad designs may similarly be used.

In general, the touch system 100 recognizes the sound created when the substrate 104 is touched at a given position that may be described as an (X,Y) coordinate location. It should be understood that other coordinate systems may be used, such as polar coordinates with a radius and angle about the origin. A touch event at each different position on the substrate 104 generates a unique sound. More specifically, when a user touches a point on the substrate 104, one or more sensors 106, 108, 110 and 112 on the substrate 104 detect the sound and represent the sound as a signal. Whether the touch panel is a transparent touchscreen or an opaque or non-opaque touchpad, the principles of acoustic fingerprinting remain the same.

An analog to digital (A/D) converter 126 within the touchscreen controller 124 receives the two analog signals produced by sensors 106-112 over the touchscreen cable 122. The A/D converter 126 outputs digitized signals 148 that are received by a processor module 128. A frequency transform module 134 may perform a frequency transform, such as a Fourier transform, or more specifically a Fast Fourier Transform (FFT), on the digitized signals 148, outputting a frequency transform data set of frequency components associated with each of the sensor signals. An amplitude magnitude module 130 may then extract amplitude magnitude information from each of the frequency transform signals, and optionally, a phase module 132 may extract phase information from each of the frequency transform signals. The processor module 128 may construct a profile or acoustic fingerprint associated with the live touch event based on the amplitude magnitude information. In another embodiment, the processor module 128 may construct a profile or acoustic fingerprint associated with the live touch event that is based on both the amplitude magnitude information and the phase information. In yet other embodiments, the processor module 128 may construct the acoustic fingerprint based on the phase information or based on other information within the digitized signals 148.

A memory 136 may store a calibration file 138 that contains a set of acoustic fingerprints to allow the user to successfully interact with the display 160. Prior to normal use, (X,Y) coordinate positions on the touch panel 102 are associated with the sensor signals that are generated when a series of known (X,Y) coordinate locations are touched. The signals may be processed and stored as acoustic fingerprints in the calibration file 138, such as first, second through N template fingerprints 140, 142 and 144. In some embodiments, the signals may be processed differently to create a second calibration file 164 that is also stored in the memory 136 and contains a corresponding set of acoustic fingerprints. In this example, first, second through N template fingerprints 174, 176 and 178 correspond to the first, second through N template fingerprints 140, 142 and 144, respectively, and thus represent the same (X,Y) coordinate positions on the touch panel 102. Embodiments utilizing both of the calibration files 138 and 164 are discussed further below. It should be understood that additional calibration files may be used.

In the context of bending-wave touchscreen technology, acoustic fingerprints may also be referred without ambiguity simply as “fingerprints.” By way of example, known location 152 may correspond to the second template fingerprint 142 and known location 154 may correspond to the Nth template fingerprint 144. The calibration file 138 there fore includes a plurality or collection of predetermined fingerprints or templates, each of which is associated with a particular (X,Y) location on the touch panel 102. Here, the term “template” refers to a predetermined fingerprint in a calibration file. In contrast, a live acoustic fingerprint computed for a run-time touch is referred to herein as a “live fingerprint” or a “real-time fingerprint”. In the discussion of some embodiments, acoustic fingerprints may also be referred to as “profiles” or “trajectories”, as discussed further below.

With acoustic fingerprinting, how the touch is generated can potentially have an undesired impact on the acquired acoustic fingerprint. For example, for two touches at the same (X,Y) coordinate location wherein the first touch is generated using a hard stylus and the second touch is generated using a rubber eraser, much different signals will be generated, and thus different acoustic fingerprints may be generated. A drag type of touch generates other types of signals. If an acoustic fingerprint is dependent on these and other touch characteristics, then for each touch position (X,Y) there will be numerous different acoustic fingerprints corresponding to different ways of touching the same point on the touch surface. Such acoustic fingerprint sensitivity to other touch characteristics diminishes the ability of a single template acoustic fingerprint to represent all touches at a given (X,Y) position. Therefore, it is desired to generate a unique template fingerprint associated with the (X,Y) location that correlates to all of the different types of signals, allowing signals from different types of touches to be mapped to the unique fingerprint. Furthermore, it may be desirable to have more than one type of template fingerprint where the template fingerprint of each type is unique for a given (X,Y) location.

In one embodiment directed to generating a unique fingerprint associated with an (X,Y) location, the first, second through N fingerprint templates 140, 142 and 144 may be based on magnitude ratios associated with two digitized signals as determined by the amplitude magnitude module 130, such as a ratio between the amplitude magnitudes of each of the two signals, or functions of the ratio. In another embodiment, the first, second through N template fingerprints 140, 142 and 144 may be based on magnitude ratios in combination with phase information determined by the phase module 132, such as phase difference information. Therefore, the acoustic fingerprint definitions may be functions of both magnitude ratios as well as phase differences.

During typical use, when a user touches the touch panel 102, the processor module 128 compares the live-touch acoustic fingerprint to at least a subset of the first, second through N template fingerprints 140, 142 and 144 stored in the calibration file 138. The best matching acoustic fingerprint or template may then be used to identify the location of the touch event.

The processor module 128 may then pass the (X,Y) coordinates to a display module 146 that may be stored within one or more modules of firmware or software. The display module 146 may be a graphical user interface (GUI) module such as the Microsoft Windows® operating system, for example. In one embodiment, the display module 146 is run on a host computer 162 that also runs an application code of interest to the user. The display module 146 determines whether the coordinates indicate a selection of a button or icon displayed on the display 160. If a button is selected, the host computer 162 or other component(s) (not shown) may take further action based on the functionality associated with the particular button.

An analogy can be drawn between acoustic fingerprinting and modern quantum field gauge theories of the fundamental forces of nature. In the field of physics research, “gauge” theories have helped quantum field theorists understand the fundamental forces of nature such as electromagnetic, weak and strong nuclear forces. Theories that have been found to correctly describe nature as observed in the laboratory come from only the very small subset of mathematical possibilities that satisfy the principle of “gauge invariance.”

While the original meaning of the term “gauge” referred to a standard for measuring mechanical dimensions, as in “wire gauge”, the meaning of “gauge” has been generalized to the non-mechanical setting of standards needed for quantitative descriptions of physical systems. In particular, a choice of “gauge” can be made when selecting a point in a circuit to label as “ground” or zero volts. While running a circuit simulation program such as SPICE, a “gauge transformation” may be performed by defining another point in the circuit as ground and rerunning the SPICE simulation. This gauge transformation will cause the numerically simulated voltage at every point in the circuit to change. Nevertheless, the SPICE program is “invariant” under this gauge transformation as the change in reference ground definition causes no changes of any predictions for electronics laboratory measurements such as a voltage difference between two points in the circuit. This “gauge invariance” may be used as a debugging and quality-check tool. Lack of such gauge invariance in preliminary circuit-simulation code, i.e. changes in testable predictions from a redefinition of the reference ground point, would flag coding errors. An example of such a coding error is an Ohm's Law calculation of the current through a resistor which incorrectly uses the absolute voltage at one resistor terminal rather than correctly uses the voltage difference between the two resistor terminals.

The principles of gauge invariance can be applied to bending-wave touch technology to sort between good and bad candidate acoustic fingerprint definitions. For touchscreen applications, the characteristic of interest is the touch location, that is, the (X,Y) coordinates of the touch. Typically, all other touch characteristics, such as force, are irrelevant to the application. If acoustic fingerprint definition is impacted by any other touch characteristic, the correct (X,Y) coordinate location may not be identified and selected, creating many problems within an application. Therefore, a “gauge invariant acoustic fingerprint” that depends only the on the touch coordinates (X,Y) is desirable.

The phase difference profile Δφ(ω) is insensitive to other identified touch characteristics while being sensitive to the touch position. This is the theoretical basis of the historical observation that the phase difference profile outperforms other candidate acoustic fingerprint definitions, such as normalized amplitude profiles.

As discussed previously, the overall strength of the touch is one touch characteristic other than touch position. For example, doubling the strength of a touch, that is, touching in the same fashion with twice the force, will increase the signal amplitudes. In this example, the signal amplitudes are doubled, that is S₁(t)→S′₁(t)=2·S₁(t) and S₂(t)→S′₂(t)=2·S₂(t). The apostrophe “'” is used to indicate a gauge transformed signal. S′₁(t) and S′₂(t) are “gauge transformed” signals due to the doubling of the touch strength. The gauge transformation, that is, the change of gauge (i.e. the change of method of touching at the touch location) has the same effect on both signals. Another touch characteristic is the exact timing of the touch. For example, the “gauge transformed” signals S₁(t)→S′₁(t)=S₁(t−Δt) and S₂(t)→S′₂(t)=S₂(t−Δt)) indicate that the gauge transformed signals S′₁(t) and S′₂(t) correspond to a touch that is delayed by a time interval Δt but is otherwise identical. Yet another characteristic of a touch is the power spectrum of the touch induced forces that excite bending waves; for example, switching from a hard stylus to a soft stylus changes the touch power spectrum. In the frequency domain, this corresponds to the “gauge transformed” signals S₁(ω)→S′₁(ω)=F(ω)·S₁(ω) and S₂(ω)→S′₂(ω)=F(ω)·S₂(ω) where F(ω) relates to the frequency characteristics of the touch force and is the ratio (in general complex) of the power-spectrum altered touch force to the unaltered touch force as a function of frequency ω. All these types of gauge transformed signals correspond to touches at exactly the same (X,Y) touch location. An acoustic fingerprint definition that is gauge sensitive, that produces different results depending on exactly how the point (X,Y) is touched, is undesirable. In contrast, a “gauge invariant” acoustic fingerprint that depends only on the touch (X,Y) coordinates and is independent of all other touch characteristics, is desired for use in acoustic fingerprint based touch systems.

Mathematical criteria for the concepts of undesired gauge sensitivity and desired gauge invariance in the context of bending-wave touch technology can be further developed with the aid of a magnitude gauge function G(ω) and a phase gauge function γ(ω). Even for a fixed touch location, a change in how a touch is generated will, in general, alter signal amplitude magnitudes and/or phases in complex ways as a function of frequency. However, any change in how one touches a given location affects both signals in the same manner. Stated in mathematical terms, in general, gauge transformed frequency domain signals are as follows.

S ₁(ω)=A ₁(ω)·e ^(iφ)1^((φ)) →A′ ₁(ω)·e ^(iφ′)1^((ω)) =G(ω)·A ₁(ω)·e ^(iφ)1^((ω)+iγ(ω))  Equ. 6

S ₂(ω)=A ₂(ω)·e ^(iφ)2^((ω)) →A′ ₂(ω)·e ^(iφ′)2^((ω)) =G(ω)·A ₂(ω)·e ^(iφ)2^((ω)+iγ(ω))  Equ. 7

The two signals S₁(ω) and S₂(ω) are changed in the same way. Equivalently the gauge transformation can be expressed as follows.

A ₁(ω)→A′ ₁(ω)=G(ω)·A ₁(ω)  Equ. 8

A ₂(ω)→A′ ₂(ω)=G(ω)·A ₂(ω)  Equ. 9

φ₁(ω)→φ′₁(ω)=φ₁(ω)+γ(ω)  Equ. 10

φ₂(ω)→′₂(ω)=φ₂(ω)+γ(ω)  Equ. 11

The previous example of doubled touch strength corresponds to a gauge transformation with the gauge functions G(ω)=2 and γ(ω)=0. Delay of the touch by a time Δt corresponds to the gauge transformation with the gauge functions G(ω)=1 and γ(ω)=ω·Δt. Changes in the touch power spectrum correspond to gauge transformations with more complex forms of the gauge functions G(ω) and γ(ω). As seen in the above equations, all the signal components A₁(ω), A₂(ω), φ₁(ω) and φ₂(ω) are sensitive to gauge transformations and hence are sensitive to changes to touch characteristics beyond (X,Y) coordinates. A gauge transformation of the phase-difference profile is shown below.

Δφ(ω)→Δφ′(ω)=φ′₂(ω)−φ′₁(ω)={φ₂(ω)+γ(ω)}−{φ₁(ω)+γ(ω)}=φ₂(ω)−φ₁(ω)=Δφ(ω)  Equ. 12

As indicated by the result Δφ(ω)→Δφ(ω), Equ. 12 illustrates that the phase-difference profile is unaffected by a gauge transformation of touch characteristics. Thus the phase-difference profile is a gauge invariant acoustic fingerprint, depending only on the touch location and not on any other characteristics of the touch. In contrast, the normalized amplitudes discussed previously are gauge sensitive and depend on the details of the power spectrum of the touch force, as shown below in Equ. 13 and 14:

a ₁(ω)→a′ ₁(ω)=A′ ₁(ω)/A′ ₁ ^(MAX) =G(ω)·A ₁(ω)/Max {G(ω)·A ₁(ω)}≠a ₁(ω)  Equ. 13

a ₂(ω)→a′ ₂(ω)=A′ ₂(ω)/A′ ₂ ^(MAX) =G(ω)·A ₂(ω)/Max {G(ω)·A ₂(ω)}≠a ₂(ω)  Equ. 14

Therefore, the phase-difference profile provides an effective acoustic fingerprint because it is a gauge-invariant acoustic fingerprint, that is, it is insensitive to uncontrolled and irrelevant touch characteristics. However, the phase-difference profile is not unique in this gauge-invariant property. In particular, the magnitude ratio A₂(ω)/A₁(ω) is also gauge invariant, that is, independent of irrelevant touch characteristics beyond the (X,Y) coordinate position, as illustrated in Equ. 15 wherein the gauge function G(ω) cancels out.

A ₂(ω)/A ₁(ω)→A′ ₂(ω)/A′ ₁(ω)={G(ω)·A ₂(ω)}/{G(ω)·A ₁(ω)}=A ₂(ω)/A ₁(ω)  Equ. 15

The result of Equ. 15 illustrates that A₂(ω)/A₁(ω)→A₂(ω)/A ₁(ω).

Any function f(x) of the magnitude ratio is also gauge-invariant.

f(A ₂(ω)/A ₁(ω))→f(A′ ₂(ω)/A′ ₁(ω))=f(A ₂(ω)/A ₁(ω))  Equ. 16

Thus, f(A₂(ω)/A₁(ω))→f(A₂(ω)/A₁(ω)). For example, if f(x)=1/x, it can be seen that A₁(ω)/A₂(ω) is also gauge invariant as expected from simply swapping the arbitrary labels “1” and “2.” In the cases wherein f(x)=(x−1)/(x+1), f(x)=(x²−1)/(x²+1), and f(x)=arctan(x), the following gauge invariant acoustic fingerprints can be determined.

(A ₂(ω)−A ₁(ω))/(A ₂(ω)+A ₁(ω))  Equ. 17

(A ₂(ω)² −A ₁(ω)²)/(A ₂(ω)² +A ₁(ω)²)  Equ. 18

arctan(A ₂(ω)/A ₁(ω))  Equ. 19

Therefore, as any function of the magnitude ratio is shown to be gauge invariant, there is a high degree of flexibility in how the magnitude ratio may be constructed to obtain the acoustic fingerprints. For coding purposes, Equ. 17-19 represent choices for an acoustic fingerprint that have an advantage of a finite range (range of −1 to +1 for Equ. 17 and 18 and range of 0 to π/2 for Equ. 19) both of which may easily be scaled, for example, to the range of an 8-bit integer. In contrast, the magnitude ratio A₂(ω)/A₁(ω) has a range from zero to infinity and thus may result in a higher level of complexity during implementation. The magnitude ratio A₂(ω)/A₁(ω) as well as various functions of the magnitude ratio f(A₂(ω)/A₁(ω)) such as (A₂(ω)−A₁(ω))/(A₂(ω)+A₁(ω)) and arctan(A₂(ω)/A₁(ω)) all provide gauge invariance; thus the term “magnitude ratio” is broadly defined to include both A₂(ω)/A₁(ω) and all functions thereof.

By way of example only, a calibration file of acoustic fingerprints may be constructed by tapping a 15-inch (approximately 380 mm) diagonal bending-wave sensor at each point in a 76×57 grid of calibration points, for a total of N=4332 calibration points. For example, the touch panel 102 of FIG. 1 may be touched at the calibration points to generate the first, second through N template fingerprints 140, 142 and 144 within the calibration file 138. At each of these 4332 calibration point locations, a pair of analog signals (such as the signal from the sensors 106 and 112 and the signal from the sensors 108 and 110) may be digitized by the A/D converter 126, frequency transformed by the frequency transform module 134 such as by using a Hamming or Hanning filter followed by an FFT. For example, for each of 900 (out of 1024) selected discrete FFT frequencies in a range from 2.1 kHz to 19.3 kHz, arctan(A₂(ω)/A₁(ω)) may be computed, scaled and rounded to an integer range from 0 to 127, and stored as 8-bit integer. The calibration file 138 of reference acoustic fingerprint templates may then be used with a matching algorithm to identify the touch locations of live signals resulting from touch events.

FIG. 2 illustrates a method for determining a location of a live touch event on a touch panel 102 based on a single acoustic fingerprint that is constructed using magnitude ratios. At 200, an operator may initiate a touch event at an (X,Y) location on the touch panel 102, such as point (X1,Y1) 150 on FIG. 1. Because magnitude ratios are used, only the touch location will be considered, and other factors such as the implement used to create the touch or the force applied do not impact the determination of the correct location. It should be noted that the placement of the sensors 106, 108, 110 and 112 on the touch panel 102 are exemplary and are not limiting.

At 201 and 202, the A/D converter 126 detects the two signals from the four sensors 106, 108, 110 and 112, which may be conveyed on traces 114, 116, 118 and 120 to touchscreen cable 122. The A/D converter 126 outputs the sets of digitized signals 148 to the processor module 128.

At 203 and 204 the frequency transform module 134 performs a frequency transform, such as a Fourier transform, and more specifically an FFT, on the sets of digitized signals 148 corresponding to the two sensor signals, and outputs first and second frequency transform data sets. At 205 and 206, the amplitude magnitude module 130 calculates amplitude magnitudes for each of the first and second frequency transform data sets over the frequency range. For example, first and second sets of amplitude magnitudes (A₁(ω) and A₂(ω)) associated with the first and second frequency transform data sets may be calculated. At 208, the amplitude magnitude module 130 determines the magnitude ratio based on the amplitude magnitudes of 205 and 206.

At 210, the processor module 128 may construct a live fingerprint based on the magnitude ratio. The live fingerprint is representative of the live touch event that includes each frequency band (that may in some embodiments be limited to a selected range) of the frequency transform. At 212, the processor module 128 compares the live fingerprint to the template fingerprints, such as the first through N template fingerprints 140-144 stored in the calibration file 138, to determine differences there-between and to identify one or more of the template fingerprint that is/are a “best match” with respect to the acoustic fingerprint. In this example, the first through N template fingerprints 140-144 are representative of the magnitude ratio as a function of frequency. The processing method that is used to acquire the live fingerprint is also used to build the template fingerprints in the calibration file used for comparison. In one embodiment, the processor module 128 may determine a score or a scoring relation for each comparison, wherein the score may be a sum of the absolute differences over the frequency range. The score represents how different, or how closely matched, the live fingerprint is with respect to each of the first through N template fingerprints 140-144 in the calibration file 138. The processor module 128 may determine the lowest score(s) that indicates one, or optionally more than one, possible candidate positions or best matching template fingerprint.

At 214 the processor module 128 may compare the difference between the best matching template fingerprint and the live fingerprint to a predetermined threshold. If the difference is greater than the predetermined threshold, the method passes to 216 and the live fingerprint is rejected. In other words, the live fingerprint does not sufficiently match any of the template fingerprints in the calibration file 138. If the difference at 214 is less than the predetermined threshold, at 218 the processor module 128 identifies the coordinates, such as (X,Y) coordinates, of the best match identified at 212. The processor module 128 may then interpolate at 220 to further refine the location of the live touch event on the touch panel 102 based on the coordinates of the best match template fingerprint and the coordinates and scores of the neighboring template fingerprints. Then at 222, the touch coordinates are reported to the host computer 162. If the live touch event corresponds to a selection on the display 160, the host computer 162 may initiate a particular action or function and/or update the display 160.

In addition to the magnitude ratio (for example A₂(ω)/A₁(ω) or any function thereof), the set of possible gauge-invariant acoustic fingerprints includes functions of both the magnitude ratio and the phase difference profile Δφ(ω). An acoustic fingerprint can incorporate both magnitude ratios and phase differences. Another approach is one in which one acoustic fingerprint uses magnitude ratios and a distinct separate acoustic fingerprint uses phase differences.

FIG. 3 illustrates a method for determining a location of a live touch event on the touch panel 102 of FIG. 1 based on two acoustic fingerprints wherein one acoustic fingerprint is constructed using magnitude ratios and another acoustic fingerprint is constructed using phase differences. The method of FIG. 3 illustrates an example of redundant validation wherein more than one acoustic fingerprint is generated based on a single stimulus or touch event. By using more than one acoustic fingerprint, the issue of noise sensitivity in small screens is better addressed. Redundant validation may be used together with one or two of the acoustic fingerprints constructed using magnitude ratio. Alternatively, magnitude ratio may be used to construct one acoustic fingerprint while phase difference may be used to construct a second acoustic fingerprint. In another embodiment, phase difference may be used to construct two acoustic fingerprints, and in yet another embodiment, a different acoustic fingerprint construction may be used to construct one or both of the acoustic fingerprints.

At 230, an operator initiates a touch event at an (X,Y) location on the touch panel 102, such as point (X1,Y1) 150 on FIG. 1 as discussed previously with FIG. 2. At 231 and 232, the A/D converter 126 detects the two signals as discussed previously and outputs the sets of digitized signals 148 to the processor module 128.

At 233 and 234 the frequency transform module 134 performs a frequency transform on the sets of digitized signals 148 corresponding to the two signals, and outputs first and second frequency transform data sets. In contrast with FIG. 2, information is extracted from the first and second frequency transform data sets based on magnitude and phase and is used to construct two separate fingerprints.

The construction of the acoustic fingerprint based on the magnitude ratio uses the process a similar process as in FIG. 2. At 235 and 237, the amplitude magnitude module 130 calculates first and second sets of amplitude magnitudes based on the frequency transform data sets of 233 and 234, respectively, over the frequency range. At 239, the amplitude magnitude module 130 determines the magnitude ratio based on the amplitude magnitudes calculated in 235 and 237. At 241, the processor module 128 may construct a first live fingerprint based on the magnitude ratio. At 243, the processor module 128 compares the first live fingerprint of the live touch event to the template fingerprints to determine differences there-between and to identify the template fingerprint that is a “best match” with respect to the live fingerprint. The template fingerprints may be the first, second through N template fingerprints 140, 142 and 144 within the calibration file 138. The processor module 128 may determine the best score(s) that indicates a possible candidate position or best matching template fingerprint.

At 245 the processor module 128 may compare the difference between the best matching template fingerprint and the live fingerprint to a predetermined threshold. If the difference is greater than the predetermined threshold, the method passes to 248, the live fingerprint is rejected and the method is terminated. If the difference at 245 is less than the predetermined threshold, at 249 the processor module 128 identifies the (X_(MR),Y_(MR)) coordinates of the best match identified at 243.

The construction of the acoustic fingerprint based on the phase difference uses data sets of 233 and 234. Phases are determined and a phase difference profile is computed. At 236 and 238, the phase module 132 determines the phase of each of the frequency transform data sets of 233 and 234, respectively, and at 240 the phase module 132 determines the phase difference between the two signals. At 242, the processor module 128 may construct a live fingerprint based on the phase difference. At 244, the processor module 128 compares the live fingerprint of the live touch event to the template fingerprints to determine differences there-between and to identify the template fingerprint that is a “best match” with respect to the live fingerprint. In this example, the template fingerprints may be the first, second through N template fingerprints 174, 176 and 178 within the calibration file 164. Again, the processor module 128 may determine the score(s) that indicates a possible candidate position or best matching template fingerprint.

At 246 the processor module 128 may compare the difference between the best matching template fingerprint and the acoustic fingerprint to a predetermined threshold. If the difference is greater than the predetermined threshold, the method passes to 248 and the acoustic fingerprint is rejected and the method is terminated. If the difference at 246 is less than the predetermined threshold, at 250 the processor module 128 identifies the (X_(PD),Y_(PD)) coordinates of the best match identified at 244.

At 252, the processor module 128 considers both the touch coordinates (X_(MR),Y_(MR)) based on the best matching magnitude-ratio template as well as the touch coordinates (X_(PD),Y_(PD)) based on the best matching phase-difference template. In this case, two independent touch coordinate estimates are computed. If the magnitude-ratio derived coordinates (X_(MR),Y_(MR)) and the phase-difference derived coordinates (X_(PD),Y_(PD)) disagree, such as being beyond a predetermined distance when compared to each other, the method flows to 254 where the coordinates are rejected and no touch coordinates are reported to the host computer 162. If at 252 the (X_(MR),Y_(MR)) and (X_(PD),Y_(PD)) coordinates are within a predetermined distance, at 256 the touch coordinates are reported to the host computer 162 where, if the live touch event corresponds to a selection on the display 160, the host computer 162 may initiate a particular action or function and/or update the display 160. Such redundancy in signal processing has been observed to greatly reduce the rate of false touches due to ambient sound noise. This method of redundant acoustic fingerprints can be generalized in various ways.

FIG. 4 illustrates a method for determining a location of a live touch event on the touch panel 102 of FIG. 1 based on a single gauge-invariant acoustic fingerprint incorporating both magnitude-ratio and phase-difference information. Some item numbers in FIG. 4 are the same as FIG. 3, indicating like processing techniques. Turning to 239 and 240, the magnitude ratio and phase difference based on the two digitized signals have been determined.

At 260, the processor module 128 constructs a single live fingerprint based on the magnitude ratio and phase difference. In particular, for any function of two variables f(x,y) where x is A₂(ω)/A₁(ω) and y is phase difference, f(A₂(ω)/A₁(ω), Δφ(ω)) is a gauge invariant acoustic fingerprint. For example, if f(x,y)=cos(y)·x or f(x,y)=sin(y)·x, the following gauge invariant acoustic fingerprints depend only in part on a magnitude ratio.

cos(Δφ(ω))·(A ₂(ω)/A ₁(ω))  Equ. 20

sin(Δφ(ω))·(A ₂(ω)/A ₁(ω))  Equ. 21

The above two expressions (Equ. 20 and 21) can be expressed as numbers of the frequency transform, and it can be shown that the two expressions are equal to the real and imaginary parts of the complex ratio S₂(ω)/S₁(ω), rather than the magnitude and phase. With a bit of further math, these two expressions (Equ. 19 and 20) can be shown to be equivalent to the below two expressions, Equ. 22 and 23.

[Re{S ₁(ω)}·Re{S ₂(ω)}+Im{S ₁(ω)}·Im{S ₂(ω)}]/[Re{S ₁(ω)}² +Im{S ₁(ω)}²]  Equ. 22

[Re{S ₁(ω)}·Im{S₂(ω)}−Im{S ₁(ω)}·Re{S₂(ω)}]/[Re{S ₁(ω)}² +Im{S ₁(ω)}²]  Equ. 23

With respect to implementation, a programmer may note that the quantities Re{S₁(ω)}, Im{S₁(ω)}, Re{S₂(ω)} and Im{S₂(ω)} as floating point or integer values are typically the raw output of FFT code. Hence the expressions above (Equ. 22 and 23) provide a gauge-invariant fingerprint that may be computed directly from FFT outputs with no use of square root or transcendental functions, only addition, subtraction, multiplication and division.

An acoustic fingerprint need not be limited to a single real variable as a function of frequency, that is a “profile,” but may be generalized to a point in two or higher dimensional space as a function of frequency. Acoustic fingerprints then become sets of points equivalent to “paths” or “trajectories” (parameterized by frequency) within the higher dimensional space. A pair of real variables as a function of frequency may be used to combine and retain all the information contained in the gauge-invariant A₂(ω)/A₁(ω) profile and the gauge-invariant phase difference profile Δφ(ω). One way to combine these two profiles is to form a 2-D vector as a function of frequency (X(ω), Y(ω)=(A₂(ω)/A₁(ω), Δφ(ω)). For a specific frequency, the corresponding pair of real numbers (A₂/A₁, Δφ) may be regarded as a point 330 (as shown in FIG. 5) in a polar-coordinate plane 302 in which the distance from the origin is the ratio A₂/A₁ and the polar angle is Δφ. The complete set of points (A₂(ω)/A₁(ω), Δφ(ω)) as a function of frequency ω geometrically defines a trajectory 332 within the polar-coordinate plane 302. For a live touch, trajectory 332 is an acoustic fingerprint that may be compared as similar or dissimilar to a template trajectory stored in a calibration file. Many options exist for constructing acoustic fingerprints in the form of such trajectories. Of particular interest are trajectory coordinate systems that support unbiased matching score algorithms.

With a sufficiently large sampling of frequencies ω the acoustic fingerprint (A₂(ω)/A₁(ω), Δφ(ω)) has been found to be effective even when the resolution is reduced to only one bit; for example the magnitude ratio A₂(ω)/A₁(ω) may be set to one if A₂(ω)>A₁(ω) and zero otherwise and Δφ(ω) reduced to its sign bit, namely one if Δφ(ω) is negative and zero otherwise. Alternatively, magnitude ratios and phase differences may be stored with 8-bit resolution (one byte) or any other resolution. If a fixed number of bits of memory are available for each fingerprint, a number of choices are available regarding a trade-off between the resolution of the fingerprint and the number of frequencies sampled to construct the fingerprint. When processing power is limited, an important advantage of one-bit resolution is that live and template fingerprints may be compared with great computation efficiency using the logical XOR function.

FIG. 5 illustrates a spherical coordinate system for visualizing such acoustic fingerprints as geometrical trajectories. A composite acoustic fingerprint may be formed from the A₂(ω)/A₁(ω) and phase-difference profiles to define a trajectory 342 on the surface of a unit sphere 300 in which the latitude θ(ω) and longitude φ(ω) are functions of frequency as discussed below. For each frequency, there is a point on the sphere such as point 340, and together all of the frequencies form the trajectory such as 342. For purposes of discussion herein, a unit sphere 300 may be referred to with geographical references typically used with the earth, such as North Pole 320, South Pole 322, Equator 324, latitude and longitude. Illustrated are the unit sphere 300 and a corresponding polar-coordinate plane 302 with polar coordinate radius equal to the magnitude ratio A₂/A₁ and polar coordinate angle equal to the phase difference φ₂−φ₁. On the unit sphere, the longitude (east/west) angle is defined by the phase-difference profile:

φ(ω)=Δφ(ω)=Δ₂(ω)−φ₁(ω)  Equ. 24

The latitude (north/south) angle is the example of a magnitude ratio defined by the following function of A₂(ω)/A₁(ω):

θ(ω)=π/2−2 arctan(A ₂(ω)/A ₁(ω))  Equ. 25

The entire polar-coordinate plane 302 may be mapped onto the spherical surface of the unit sphere 300 so that the point 330 in the polar-coordinate plane 302 is mapped to the point 340 in the unit sphere 300 and plane trajectory 332 is mapped to sphere trajectory 342. Swapping the arbitrary signal labels “1” and “2” generally changes the distance in the polar-coordinate plane 302 between corresponding points (same frequency) of live and template trajectories, which results in an undesirable condition for score calculations. In contrast, scores based on distances between trajectory points in the unit sphere 300 are free of such biases. Swapping the roles of the first and second sensor signals (as represented by the signals from the sensors 106 and 108 of FIG. 1) only flips the North and South Poles 320 and 322 and rotates the unit sphere 300 by 180° about the polar axis; distances between any two points on the sphere 300 are unchanged. Furthermore, via Monte Carlo simulations, it has been observed that if there is no touch signal and the signals S₁(t) and S₂(t) are simply random noise, then the corresponding values for (θ(ω), φ(ω)) are uniformly and randomly distributed on the surface of the sphere. This implies that a combined magnitude-ratio and phase-difference acoustic fingerprint is represented by the above equations in an unbiased way as a trajectory (as a function of frequency) on the two-dimensional surface of a unit sphere. Due to the image of a path or trajectory on a surface or higher dimensional space, acoustic fingerprints that provide more than one value per frequency may be referred to as “trajectories” as distinct from acoustic fingerprints that provide only one value per frequency which historically have sometimes been referred to as “profiles”.

FIG. 6 illustrates in more detail the mapping of the ratio A₂/A₁ to a latitude angle θ=π/2−2 arctan(A₂/A₁). By way of example, a point proximate to North Pole 320 may correspond to signals S₁ and S₂ (at a given frequency) received from the sensors 106 and 112 and the sensors 108 and 110, respectively, wherein the signal amplitude magnitude A₁ is very large compared to signal amplitude magnitude A₂, in which case the signal from the sensors 108 and 110 may simply be noise. A point proximate to South Pole 322 may correspond to signals S₁ and S₂ wherein the signal amplitude magnitude A₂ is very large compared to the signal amplitude magnitude A₁, in which case the signal from sensors 106 and 112 may simply be noise. A point proximate to Equator 324 is a location where signal amplitude magnitudes A₁ and A₂ are approximately equal, that is sensors 106 and 112 generate a signal of amplitude comparable to the signal amplitude from sensors 108 and 110.

Each point of a trajectory based on the live touch may be compared to a corresponding point representing the same frequency of a trajectory of a template fingerprint to determine how close or how far apart the corresponding points are on the unit sphere. Making such a comparison for all points, i.e. for all frequencies, of the pair of trajectories provides a measure of how similar or different the two trajectories, i.e. acoustic fingerprints, are. Such a distance of separation between corresponding trajectory points on the unit sphere provides an unbiased sense of distance that is not provided by the ratio A₂/A₁ or the phase difference φ₂(ω)−φ₁(ω) by itself. If S₂ is very small compared to S₁ so that A₂/A₁ is essentially zero and S₂ is noise, φ₂(ω) becomes a random value and the phase difference φ₂(ω)−φ₁(ω) becomes a random value in the full range from plus to minus 180 degrees. Thus φ₂(ω)−φ₁(ω) is a biased measure of difference in which small random noise fluctuations can sometimes falsely imply big differences in acoustic fingerprints. For amplitudes, a value of A₂ twice as large as A₁, A₂=2·A₁ (or A₂/A₁=2), deviates from equality, A₂=A₁ (or A₂/A₁=1) no more or less than for a value of A₁ twice as large as A₂, A₁=2·A₂ (or A₂/A₁=½). If the values A₂/A₁=2, 1 and ½ are plotted on a number line 326 as is done in FIG. 6, the false impression is given that A₂/A₁=2 is further from A₂/A₁=1 than A₂/A₁=½ is from A₂/A₁=1. As seen in FIG. 6 this bias is removed by mapping the ratio A₂/A₁ to a latitude angle θ=π/2−2 arctan(A₂/A₁).

In practical code, angles in radians, such as the longitude angle φ(ω) of Equ. 24, may be scaled and offset for ease of computation and data storage. For example, the −π to π radian range φ(ω) may be scaled to the signed 8-bit (one byte) integer range from −128 to +127 by multiplying φ(ω) by the scaling factor (128/π). Likewise, the latitude angle θ(ω) of Equ. 25 can also be scaled (i.e. multiplied by a constant) by the same factor (128/n), in which case the −π/2 to +π/2 radian range θ(ω) is scaled to the range −64 to +63. If positive integers are preferred, this in turn can be offset to the range from 0 to 127 by simply adding the constant 64. More generally, any acoustic fingerprint may be scaled (multiplied by a constant) or offset (added to a constant) without departing from the scope of the invention.

Returning to FIG. 4, at 262 the processor module 128 may compare the live-signal trajectory (the acoustic fingerprint of 260) to trajectories corresponding to templates stored in one of the calibration files. In this case, the first, second through N template fingerprints 140, 142 and 144 of the calibration file 138 store template trajectories based on both magnitude ratio and phase-difference. When using both the magnitude ratio and the phase difference to determine the location of a live touch (X,Y), in addition to the live-signal trajectory (θ(ω),φ(ω)), a template trajectory (Θ(ω),Φ(ω)) on the surface of a unit sphere 300 corresponds to a calibration touch at a location (X₀,Y₀). The processor module 128 calculates a mismatch distance for all frequencies. The mismatch distance as a function of frequency is calculated between the live-signal trajectory and each of the first, second through N template fingerprints 140, 142 and 144, and may be calculated using Equ. 25 as discussed below.

The proximity of the live touch coordinates (X,Y) to the calibration touch coordinates (X₀,Y₀) is judged by the degree of similarity of the live-signal acoustic fingerprint (θ(ω),φ(ω)) to the template fingerprint (Θ(ω),Φ(ω)). At each frequency ω, the live-touch and the template correspond to the points (θ(ω),φ(ω)) and (Θ(ω),Φ(ω)) on the unit sphere 300. α(ω) is the shortest distance on the sphere 300 between the two points, and is also the (positive) angle in radians between unit vectors from the center of the sphere 300 to each of the two points (θ(ω),(φ(ω)) and (Θ(ω),Φ(ω)) on the surface of the unit sphere. Equ. 26 allows the calculation of the mismatch distance parameter α(ω) from the live-signal acoustic fingerprint (θ(ω),(φ(ω)) and the template acoustic fingerprint (Θ(ω),(Φ(ω)) (stored as first, second through N template fingerprints 140, 142 and 144).

cos(α(ω)=cos((θ(ω)−Θ(ω))+cos(θ(ω))·cos(Θ(ω))·cos(φ(ω)−Φ(ω))  Equ. 26

In practice, the distance parameter α(ω) is computed for a large number of discrete values (Ok where k is an integer index. The sum over frequencies of the distance parameter, namely Σα(ω_(k)), provides an overall score of the degree of mismatch between the live-signal and one, some or all of the first, second through N template fingerprints 140, 142 and 144. Mathematically, the scoring function may be represented as the following integral.

$\begin{matrix} {\int{{\alpha (\omega)}{\omega}}} & {{Equ}.\mspace{14mu} 27} \end{matrix}$

Furthermore, if f(x) is any monotonically increasing function for 0<x<π, ∫f(α(ω))dω is also a possible scoring function. Both ∫{1−cos(α(ω))}dω and ∫√{1−cos(α(ω)}dω, for example, are possible options given the above explicit formula for cos(α(ω_(k))). At 264 of FIG. 4 the processor module 128 identifies the (X,Y) coordinates of the best match identified at 262. Optionally, thresholding may be accomplished as discussed previously with FIGS. 2 and 3. At 266, the processor module 128 may accomplish interpolation as previously discussed, and at 268 the processor module 128 reports the touch coordinates to the host computer 162.

Gauge invariant acoustic fingerprints are also useful in bending-wave touch systems with more than two signals. Therefore, more than two signals may be used to construct one, two or more acoustic fingerprints. In general, more than two signals are received from which one or more acoustic fingerprints are constructed. The acoustic fingerprints may be constructed based on phase-difference, magnitude ratio, a different construction, and/or a combination thereof. If more than one acoustic fingerprint is constructed, the benefits of redundant validation may be realized. As discussed previously with FIG. 3, redundant validation provides robust touch point detection in all sizes of touchscreens and especially for smaller-sized touchscreens and touchscreens used in noisy environments.

FIG. 7 illustrates an acoustic fingerprint based touch system 400 having a touch panel 401 that generates two or more signals that are used for touch detection. The touch panel 401 has a substrate 402 upon which first, second through M sensors 404, 406 and 408 are mounted. In FIG. 7, a single sensor 404, 406 and 408 is used to produce a signal rather than a pair of piezos or microphones interconnected in anti-series (as shown on FIG. 1). Traces 410 and 412 convey a first signal associated with the first sensor 404, traces 414 and 416 convey a second signal associated with the second sensor 406, and traces 418 and 420 convey an Mth signal associated with the M sensor 408. Cable 422 carries the signals to the touchscreen controller 124 as discussed previously in FIG. 1. The elements within the touchscreen controller 124, display 160 and host computer 162 of FIG. 1 are reproduced in connection with the touch system 400.

Generalizing the concept of a gauge transformation, touch signals subject to changes in irrelevant touch characteristics may be represented as a set of digitized signals S₁(t), S₂(t) . . . S_(M)(t) where M is any integer greater than or equal to two. The corresponding frequency transform signals are S₁(ω), S₂(ω) . . . S_(M)(ω). Given an amplitude magnitude gauge function G(ω) and a phase gauge function γ(ω), the gauge transformed signals are as follows.

S(ω)=A ₁(ω)·e ^(iφ)1^((ω)) →A′ ₁(ω)·e ^(iφ′)1^((ω)=) G(ω)·A ₁(ω)·e ^(iφ)1^((ω)+iγ(ω))  Equ. 28

S ₂(ω)=A ₂(ω)·e ^(iφ)2^((ω)→) A′ ₂(ω)·e ^(iφ′)2^((ω)) =G(ω)·A ₂(ω)·e ^(iφ)2^((ω)+iγ(ω))  Equ. 29

. . . through . . .

S _(M)(ω)=A _(M)(ω)·e ^(iφ) M ^((ω)) →A′ _(M)(ω)·e ^(iφ′) M ^((ω)=) G(ω)·A _(M)(ω)·e ^(iφ) M ^((ω)+iγ(ω))  Equ. 30

Equivalently, the following is given.

A ₁(ω)→A′ ₁(ω)=G(ω)·A ₁(ω) φ₁(ω)→φ′₁(ω)=φ₁(ω)+γ(ω)  Equ. 31

A ₂(ω)→A′ ₂(ω)=G(ω)·A ₂(ω) φ₂(ω)→φ′₂(ω)=φ₂(ω)+γ(ω)  Equ. 32

. . . through . . .

A _(M)(ω)→A′ _(M)(ω)=G(ω)·A _(M)(ω) φ_(m)(ω)→φ′_(M)(ω))=φ_(M)(ω)+γ(ω)  Equ. 33

To construct a generalized gauge-invariant magnitude ratio, let α₁, α₂, . . . α_(M) be a set of exponents that sum to zero, that is Σα_(i)=0. A generalized magnitude ratio may be defined to be the following product.

A ₁(ω)^(α1) ·A ₂(ω)^(α2) · . . . ·A _(M)(ω)^(αM)  Equ. 34

Under a gauge transformation, this product is gauge-invariant as is shown below.

$\begin{matrix} {\left. {{A_{1}(\omega)}^{\alpha \; 1} \cdot {A_{2}(\omega)}^{\alpha \; 2} \cdot \ldots \cdot {A_{N}(\omega)}^{\alpha \; M}}\rightarrow{\left\{ {{G(\omega)} \cdot {A_{1}(\omega)}} \right\}^{\alpha \; 1} \cdot \left\{ {{G(\omega)} \cdot {A_{2}(\omega)}} \right\}^{\alpha \; 2} \cdot \ldots \cdot \left\{ {{G(\omega)} \cdot {A_{N}(\omega)}} \right\}^{\alpha \; M}} \right. = {{\left\{ {{G(\omega)}^{\alpha \; 1} \cdot {G(\omega)}^{\alpha \; 2} \cdot \ldots \cdot {G(\omega)}^{\alpha \; M}} \right\} \cdot \left\{ {{A_{1}(\omega)}^{\alpha \; 1} \cdot {A_{2}(\omega)}^{\alpha \; 2} \cdot \ldots \cdot {A_{N}(\omega)}^{\alpha \; M}} \right\}} = {{{G(\omega)}^{{\alpha \; 1} + {\alpha \; 2} + \ldots + {\alpha \; M}} \cdot \left\{ {{A_{1}(\omega)}^{\alpha \; 1} \cdot {A_{2}(\omega)}^{\alpha \; 2} \cdot \ldots \cdot {A_{N}(\omega)}^{\alpha \; M}} \right\}} = {{{G(\omega)}^{\Sigma \; \alpha \; 1} \cdot \left\{ {{A_{1}(\omega)}^{\alpha \; 1} \cdot {A_{2}(\omega)}^{\alpha \; 2} \cdot \ldots \cdot {A_{N}(\omega)}^{\alpha \; M}} \right\}} = {{{G(\omega)}^{0} \cdot \left\{ {{A_{1}(\omega)}^{\alpha \; 1} \cdot {A_{2}(\omega)}^{\alpha \; 2} \cdot \ldots \cdot {A_{N}(\omega)}^{\alpha \; M}} \right\}} = {{A_{1}(\omega)}^{\alpha \; 1} \cdot {A_{2}(\omega)}^{\alpha \; 2} \cdot \ldots \cdot {A_{N}(\omega)}^{\alpha \; M}}}}}}} & {{Equ}.\mspace{14mu} 35} \end{matrix}$

Likewise, for any function f(x), f(A₁(ω)^(α1)·A₂(ω)α² · . . . ·A_(N)(ω)^(αM)) is also a gauge-invariant acoustic fingerprint. For M=2, α₁=−1 and α₂=+1, this form reduces to the previous magnitude-ratio expression A₂(ω)/A₁(ω). For three digitized signals, one example is M=3, α₁=+1, α₂=+1 and α₃=−2 which corresponds to a generalized magnitude-ratio for three signals:

A ₁(ω)·A ₂(ω)/A ₃(ω)²  Equ. 36

Furthermore if f(x, y, . . . z) is any function of multiple variables, each argument may be replaced by a different generalized magnitude-ratio and the resulting acoustic fingerprint will still be gauge invariant. Therefore, acoustic fingerprints may be based at least in part on any function of such generalized magnitude ratios. Consistent with the purpose of providing gauge invariance, the term magnitude ratio is broadly defined to include generalized magnitude ratios as well as functions of one or more generalized magnitude ratios.

Any magnitude ratio, including generalized magnitude ratios, can be used to construct a single acoustic fingerprint corresponding to a single touch coordinate determination. FIG. 8 illustrates a method for determining a location of a live touch event on the touch panel 401 of FIG. 7 based on one live fingerprint that is constructed based on two or more digitized signals. At 450, an operator initiates a touch event at an (X,Y) location on the touch panel 401 that has first, second through M sensors 404, 406 and 408. The touch event may be the point (X2, Y2) 424. At 452, 453 and 454 the A/D converter 126 detects the first though M signals as discussed previously and outputs corresponding sets of digitized signals 148 to the processor module 128.

At 456, 457 and 458 the frequency transform module 134 performs a frequency transform on the sets of digitized signals 148 corresponding to the first through M sets of digitized signals, and outputs first, second through M frequency transform data sets. The information extracted from the first, second through M frequency transform data sets is used to construct one live fingerprint. At 460 the processor module 128 constructs one or more gauge invariant functions of frequency, such as first, second through Kth gauge invariant functions of frequency 461, 462 and 463. By way of example, at least one of the gauge invariant functions may be based on a magnitude ratio as discussed previously. The gauge invariant functions of frequency may also be constructed from phase differences and magnitude ratios as discussed previously as well as the generalized phase differences discussed later.

At 464, the processor module 128 constructs a live fingerprint (such as a multi-dimensional trajectory) based on the first, second through Kth gauge invariant functions of frequency. At 466, the processor module 128 compares the live fingerprint of the live touch event to the template fingerprints (such as the first, second through N template fingerprints 140, 142 and 144) to determine differences there-between and to identify the template fingerprint that is a “best match” with respect to the live fingerprint. As discussed, the processor module 128 may determine the lowest score(s) that indicates a possible candidate position or best matching template fingerprint.

Optionally, thresholding may be used as discussed previously at 214 of FIG. 2. At 468, the processor module 128 identifies the (X,Y) coordinates of the best match identified at 466. At 470 the touch coordinates are reported to the host computer 162 where, if the live touch event corresponds to a selection on the display 160, the host computer 162 may initiate a particular action or function and/or update the display 160.

In one embodiment, two or more estimates of touch coordinates may be determined by constructing two or more distinct acoustic fingerprints each of which is used to independently determine a candidate touch position via its own matching score. As noted above, false touches are suppressed by requiring consistency of such redundantly computed coordinates. For example, consider an acoustic fingerprint based touchscreen system using, for example, the touch panel 401 of FIG. 7 wherein M=3 sensors (first, second, third (M) sensors 404, 406 and 408) and first though third digitized and frequency transformed signals S₁(ω), S₂(ω), and S₃(ω) (as discussed in 456, 457 and 458 of FIG. 8), from which amplitude magnitudes A₁(ω), A₂(ω), and A₃(ω) are derived. Estimated touch coordinates (X_(E1),Y_(E1)), (X_(E2),Y_(E2)) and (X_(E3),Y_(E3)) can be independently computed using first through third separate live fingerprints constructed from the gauge-invariant ratios A₂(ω)/A₁(ω), A₃(ω)/A₂(ω), and A₁(ω)/A₃(ω) respectively. Three-way agreement may be required before reporting a touch to the host computer 162. Alternatively, agreement of two out of three may be deemed sufficient for background noise suppression.

Therefore, acoustic fingerprints may be constructed purely from magnitude ratio profiles. Alternatively, acoustic fingerprints may be constructed from a combination of both magnitude-ratio and phase-difference profiles. An example is cos(Δφ)·A₂(ω)/A₁(ω). In the case of more than two digitized signals, phase differences may be generalized as follows. For example, φ₁(ω), φ₂(ω), . . . φ_(M)(ω) are signal phases as a function of frequency for M digitized signals and C₁, C₂, . . . C_(M) is a set of coefficients that sum to zero, ΣC_(i)=0. The generalized phase differences may be defined to be of the form C₁·φ₁(ω)+C ₂·φ₂(ω) . . . +C _(M)·φ_(M)(ω). Using Equations 31, 32 and 33 it can be shown that generalized phase differences are gauge invariant. For example, the quantity φ₁(ω)+φ₂(ω)−2·φ_(M)(ω) is one possible generalized phase difference for three signals. Therefore, acoustic fingerprint definitions may be functions of both magnitude ratio profiles as well as generalized phase differences. Consistent with the purpose of providing gauge invariance, the term phase difference is broadly defined to include generalized phase differences as well as functions of one or more generalized phase differences.

Any phase difference, including generalized phase differences, further increases the options for redundant coordinate estimates. For example, an acoustic fingerprint based touch system may have three or more sensors and three digitized and frequency transformed signals S₁(ω), S₂(ω), and S₃(ω), from which amplitude magnitudes A₁(ω), A₂(ω), and A₃(ω) as well as phases φ₁(ω), φ₂(ω), and φ₃(ω) are derived. Two or more acoustic fingerprints may be constructed from, for example, two or more of the following functions of frequency: A₂(ω)/A₁(ω), A₃(ω)/A₂(ω), A₁(ω)/A₃(ω), A₁(ω)·A₂(ω)/A₃(ω)², A₂(ω)·A₃(ω)/A₁(ω)², A₃(ω)·A₁(ω)/A₂(ω)², (φ₁(ω)−φ₂(ω)), (φ₂(ω)−φ₃(ω)), (φ₃(ω)−φ₁(ω)), φ₁(ω)+φ₂(ω)−2·φ₃(ω), and the like, to produce two or more estimated touch coordinates If three or more redundant coordinate calculations are performed, then reporting of touch coordinates to the host computer 162 may be determined by any level of agreement, e.g. three out of four.

FIG. 9 illustrates a method for determining a location of a live touch event on the touch panel 401 of FIG. 7 based on two or more acoustic fingerprints that are constructed based on two or more digitized signals. At 500, an operator initiates a touch event at an (X,Y) location on the touch panel 401, such as point (X2,Y2) 424. At 502, 504 and 506, the A/D converter 126 detects the first, second through M signals as discussed previously and outputs first, second through M sets of digitized signals 148 to the processor module 128.

At 508, 509 and 510 the frequency transform module 134 performs a frequency transform on first, second though M digitized signals 148 corresponding to the first, second through M sensor signals, and outputs first, second through M frequency transform data sets. In this example, information may be extracted from the first, second through M frequency transform data sets based on magnitude and phase to construct two or more separate fingerprints. However, it should be understood that one, two, less than M, or more than M fingerprints may be constructed based on magnitude ratio and/or phase difference and that other acoustic fingerprint construction may be used.

At 512, 514 and 516 the amplitude magnitude module 130 calculates first, second and M sets of amplitude magnitudes based on the frequency transform data sets of 508, 509, and 510, respectively, over the frequency range. At 518, the processor module 128 may construct at least one live fingerprint based on magnitude ratio. The amplitude magnitude module 130 determines the magnitude ratio based on the amplitude magnitudes, and may combine different signals such as determining a first magnitude ratio based on the first and Mth amplitude magnitudes and a second magnitude ratio based on the second and Mth amplitude magnitudes. Therefore, any combination of signals may be used, and some signals may be utilized more than once.

A live fingerprint based on the phase difference can be constructed by determining phases and computing a phase difference profile. At 513, 515 and 517, the phase module 132 determines the phase of each of the first, second through M frequency transform data sets, and at 518 the processor module 128 may construct a live fingerprint based on the phase difference. The phase module 132 determines the phase difference between two or more signals, and any grouping of two or more signals may be used.

The processor module 128 outputs first, second through Pth live fingerprints 520, 521 and 522. At 524, 525 and 526, the processor module 128 compares the corresponding first, second through Pth live fingerprint of the live touch event to the template fingerprints to determine differences there-between and to identify the first, second through Pth template fingerprints that are “best matches” with respect to the first, second through Pth live fingerprints. In this example, the template fingerprints may be the first, second through N template fingerprints 140, 142 and 144 within the calibration file 138, the first, second through N template fingerprints 174, 176 and 178 within the calibration file 164, or the fingerprints within an additional or other calibration file (not shown). Again, the processor module 128 may determine a score(s) (which may be one of a lowest or highest score) that indicates a possible candidate position or best matching template fingerprint.

At 528, 529 and 530 the processor module 128 may compare the difference between the best matching template fingerprints and the live fingerprint to a predetermined threshold. Optionally, thresholding may be accomplished for only one of the best matching template fingerprints and the live fingerprint. Optionally, no thresholding may be used.

When thresholding is used, if the difference is greater than the predetermined threshold, the method passes to 532 and the live fingerprint is rejected. Optionally, the method may be terminated based on a single rejection. In another embodiment, the method may be terminated if more than one rejection is identified or if a predetermined percentage of live fingerprints are rejected. If the difference at 528, 529 and/or 530 is less than the predetermined threshold, at 534, 535 and 536 the processor module 128 identifies the (X,Y) coordinates of the best match(es) identified at 524, 525 and 526, respectively.

At 538 the processor module 128 determines if there is sufficient agreement between the detected (X,Y) coordinates. For example, the processor module 128 may compare each of the coordinates to each other. If one or more coordinates are beyond a predetermined distance from the others, the processor module 128 may determine that the coordinates are erroneous and discard the particular coordinates. In another embodiment, the processor module 128 may compute an average X and an average Y value based on all of the coordinate points. Then, the Euclidean distance may be computed between the average point and each of the individual points. If the maximum distance of any point is less than a predetermined distance from the average, or, for example, within a diameter of a circle having a predetermined size formed around the average (X,Y) point, the particular point may be kept as a point that agrees. Points outside of the diameter of the circle may be discarded. If a sufficient number of points agree, such as a majority or a predetermined percentage of the points, the processor module 128 determines that a valid touch has been detected and at 540 reports a set of touch coordinates to the host computer 162. The set of touch coordinates may be the average coordinates for example. If the processor module 128 determines that agreement is not met, the method passes to 542 where the coordinates are rejected and no touch coordinates are reported to the host computer 162. This may be the case wherein a noisy environment causes false touches to be detected.

It is to be understood that the above description is intended to be illustrative, and not restrictive. For example, the above-described embodiments (and/or aspects thereof) may be used in combination with each other. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the invention without departing from its scope. While the dimensions and types of materials described herein are intended to define the parameters of the invention, they are by no means limiting and are exemplary embodiments. Many other embodiments will be apparent to those of skill in the art upon reviewing the above description. The scope of the invention should, therefore, be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled. In the appended claims, the terms “including” and “in which” are used as the plain-English equivalents of the respective terms “comprising” and “wherein.” Moreover, in the following claims, the terms “first,” “second,” and “third,” etc. are used merely as labels, and are not intended to impose numerical requirements on their objects. Further, the limitations of the following claims are not written in means—plus-function format and are not intended to be interpreted based on 35 U.S.C. § 112, sixth paragraph, unless and until such claim limitations expressly use the phrase “means for” followed by a statement of function void of further structure. 

1. A method of detecting a touch event on a touch panel, comprising: obtaining at least first and second signals from at least two sensors, where the at least first and second signals are responsive to a touch event; calculating a first amplitude magnitude associated with the first signal and calculating a second amplitude magnitude associated with the second signal; determining a magnitude ratio between the first and second amplitude magnitudes; and identifying a touch location based on the magnitude ratio.
 2. The method of claim 1, wherein the calculating includes calculating, as a function of frequency, first and second sets of amplitude magnitudes associated with the first and second signals, respectively.
 3. The method of claim 1, wherein the calculating includes calculating a series of magnitude ratios as a function of frequency.
 4. The method of claim 1, further comprising: digitizing the first and second signals to form first and second sets of digitized signals; and performing a frequency transform upon the first and second sets of digitized signals to form first and second frequency transform data sets of frequency components, the first and second amplitude magnitudes being calculated based on the first and second frequency transform data sets.
 5. The method of claim 1, wherein the identifying the touch location includes analyzing the magnitude ratio in connection with a calibration file of template magnitude ratios, wherein each of the template magnitude ratios is associated with a known location on the touch panel.
 6. The method of claim 1, wherein the identifying the touch location further comprises comparing the magnitude ratio to a plurality of template magnitude ratios within a calibration file to determine associated scoring relations, wherein each of the template magnitude ratios is associated with a known location on the touch panel, the identifying the touch location further comprising identifying at least one of the template magnitude ratios based on the scoring relations.
 7. The method of claim 1, further comprising a third signal, wherein the magnitude ratio is determined based on amplitude magnitudes associated with the first, second and third signals.
 8. A touch system, comprising: a touch panel; at least two sensors to obtain at least two signals associated with a touch event on the touch panel; and a processor module to perform frequency transforms based on the at least two signals to form frequency transform data sets, the processor module producing amplitude magnitudes based on the frequency transform data sets, the processor module comparing a ratio of the amplitude magnitudes associated with at least two of the frequency transform data sets to a calibration file of template fingerprints associated with known locations on the touch panel to determine a coordinate location associated with the touch event, the template fingerprints being at least in part based on magnitude ratios.
 9. The system of claim 8, wherein the ratio of the amplitude magnitudes represents at least a portion of a live fingerprint associated with the touch event.
 10. The system of claim 8, wherein the processor module obtains a phase difference based on the frequency transform data sets, the phase difference and the magnitude ratio being used to construct a live fingerprint associated with the touch event.
 11. The system of claim 10, comprising M signals wherein M is at least three.
 12. The system of claim 11, wherein the processor module produces a phase φ₁(ω), φ₂(ω) . . . φ_(M)(ω) for each of the M signals, wherein a linear-combination constant C₁, C₂, . . . C_(M) is associated with each of the M phases and wherein the sum of the M linear-combination constants is zero, the processor module forming a gauge-invariant phase difference C₁·φ₁(ω)+C₂·φ₂(ω) . . . +C_(N)·φ_(N)(ω) based on the sum of the products of each linear-combination constant and phase.
 13. The system of claim 10, wherein the live fingerprint is a trajectory having two dimensions wherein a first coordinate of the trajectory is based on π/2−2 arctan(A₂(ω)/A₁(ω)) and a second coordinate of the trajectory is based on Δφ(ω)=φ₂(ω)−φ₁(ω).
 14. The system of claim 10, wherein the template fingerprints within the calibration file are further based on phase differences associated with the known locations on the touch panel, the processor module generating scores based on a comparison between the live fingerprint and the template fingerprints.
 15. The system of claim 10, wherein the processor module at least one of calculates and stores with one-bit resolution at least one of the magnitude ratio and the phase difference.
 16. The system of claim 8, wherein the live fingerprint is at least one of a function of (A₁(ω)/A₂(ω)), a function of (A₂(ω)−A₁(ω))/(A₂(ω)+A₁(ω)), a function of (A₂(ω)²−A₁(ω)²)/(A₂(ω)²+A₁(ω)²), and a function of arctan(A₂(ω)/A₁(ω)), wherein A₁(ω) is a first amplitude magnitude as a function of frequency and A₂(ω) is a second amplitude magnitude as a function of frequency.
 17. A computer readable medium for use in an acoustic fingerprint based touch system, comprising: instructions to process digitized signals from at least two sensors where the digitized signals are responsive to a touch event on a touch panel, the digitized signals comprising at least first and second signals; instructions to calculate a first amplitude magnitude associated with the first signal and a second amplitude magnitude associated with the second signal; and instructions to identify a touch location associated with the touch event based on a magnitude ratio that is based on the first and second amplitude magnitudes.
 18. The computer readable medium of claim 17, further comprising instructions to compare the magnitude ratio to a calibration file of template magnitude ratios associated with known locations on the touch panel.
 19. The computer readable medium of claim 17, further comprising: instructions to calculate first phase information associated with the first signal and second phase information associated with the second signal; and instructions to further identify the touch location associated with the touch event based on phase differences between the first and second phase information.
 20. The computer readable medium of claim 17 further comprising instructions to compute a calibration file of template fingerprints associated with known locations on the touch panel, the template fingerprints being based on at least amplitude magnitude information associated with each of the known locations, the touch location being further identified based on a comparison of the magnitude ratio to the template fingerprints. 